Transient in-cylinder air flow estimation of gasoline engine using MAP learning approach

Accurate in-cylinder air mass flow estimation is extremely important for gasoline engines to reduce emissions and increase fuel economy. Since the operating conditions of gasoline engines are changeable because of the complex traffic conditions, it will lead to the precision of air mass flow estimation decreases during transient conditions compared to steady-state conditions. In order to improve the estimation accuracy of in-cylinder air mass flow, the method based on MAP learning algorithm is presented in this paper mainly considering the transient conditions. The proposed method is designed to identify and correct the volumetric efficiency MAP real time during the engine operation, which aims at increasing the estimation accuracy of volumetric efficiency under different operating conditions to improve the transient inlet flow estimation result indirectly. At last, an engine bench test is conducted to verify the estimation effect of the proposed method.